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Found 13 papers, 6 papers with code
Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection
First, instead of learning the continuous representations, we preserve the typical normal patterns as discrete iconic prototypes, and confirm the importance of Vector Quantization in preventing the model from falling into the shortcut.
Prototypes-oriented Transductive Few-shot Learning with Conditional Transport
Transductive Few-Shot Learning (TFSL) has recently attracted increasing attention since it typically outperforms its inductive peer by leveraging statistics of query samples.
Near Optimal Private and Robust Linear Regression
Under label-corruption, this is the first efficient linear regression algorithm to guarantee both $(\varepsilon,\delta)$-DP and robustness.
Machine Learning-Aided Efficient Decoding of Reed-Muller Subcodes
Next, we derive the soft-decision based version of our algorithm, called soft-subRPA, that not only improves upon the performance of subRPA but also enables a differentiable decoding algorithm.
DP-PCA: Statistically Optimal and Differentially Private PCA
For sub-Gaussian data, we provide nearly optimal statistical error rates even for $n=\tilde O(d)$.
Differential privacy and robust statistics in high dimensions
The key insight is that if we design an exponential mechanism that accesses the data only via one-dimensional robust statistics, then the resulting local sensitivity can be dramatically reduced.
KO codes: Inventing Nonlinear Encoding and Decoding for Reliable Wireless Communication via Deep-learning
In this paper, we construct KO codes, a computationaly efficient family of deep-learning driven (encoder, decoder) pairs that outperform the state-of-the-art reliability performance on the standardized AWGN channel.
Robust and Differentially Private Mean Estimation
In statistical learning and analysis from shared data, which is increasingly widely adopted in platforms such as federated learning and meta-learning, there are two major concerns: privacy and robustness.
Reed-Muller Subcodes: Machine Learning-Aided Design of Efficient Soft Recursive Decoding
To lower the complexity of our decoding algorithm, referred to as subRPA in this paper, we investigate different ways for pruning the projections.
Information Theory Information Theory
MACE: A Flexible Framework for Membership Privacy Estimation in Generative Models
In this work, we propose the first formal framework for membership privacy estimation in generative models.
Adaptive Mixture Regression Network with Local Counting Map for Crowd Counting
The crowd counting task aims at estimating the number of people located in an image or a frame from videos.
Minimax Optimal Estimation of Approximate Differential Privacy on Neighboring Databases
We pose it as a property estimation problem, and study the fundamental trade-offs involved in the accuracy in estimated privacy guarantees and the number of samples required.
Minimax Rates of Estimating Approximate Differential Privacy
We pose it as a property estimation problem, and study the fundamental trade-offs involved in the accuracy in estimated privacy guarantees and the number of samples required.
Information Theory Information Theory
